TOMLAB OPTIMIZATION ENVIRONMENT: minlpAssign

minlpAssign

Purpose

For setting up a mixed-integer nonlinear problem.

Syntax

   Prob = minlpAssign(f, g, H, HessPattern, x_L, x_U, Name, x_0, ...
                      IntVars, VarWeight, fIP, xIP, ...
                      A, b_L, b_U, c, dc, d2c, ConsPattern, c_L, c_U,... 
                      x_min, x_max, f_opt, x_opt);

Description

minlpAssign implements the TOMLAB Quick (TQ) format for unconstrained and constrained mixed-integer nonlinear programming problems.

minlpAssign is setting the variables normally needed for an optimization in the TOMLAB structure Prob.

Input Parameters

Call with at least seven parameters

                                      
 f           Name of the function that computes the function value f(x) 
 g           Name of the function that computes the n x 1 gradient vector
 H           Name of the function that computes the n x n Hessian matrix

 HessPattern n x n zero-one sparse or dense matrix, where 0 values indicate 
             zeros in the Hessian and ones indicate values that might 
             be non-zero. If empty indicates estimation of all elements 
             HessPattern is used when estimating the Hessian numerically.

 x_L         Lower bounds on parameters x. If [] set as a nx1 -Inf vector.
 x_U         Upper bounds on parameters x. If [] set as a nx1  Inf vector.

 Name        The name of the problem (string)

 x_0         Starting values, default nx1 zero vector 

 Note:       The number n of the unknown variables x are taken as
             max(length(x_L),length(x_U),length(x_0))
             You must specifiy at least one of these with correct length,
             then the others are given default values

The following parameters are optional, and problem type dependent. Set empty to get default value.

 IntVars     The set of integer variables. Can be given in one of three ways:

             1) a scalar N<=n, in which case variables x(1)-x(N) are 
                restricted to integer values.

             2) a vector of indices, e.g. [1 2 5]

             3) a 0-1 vector of length n=length(x) where nonzero elements    
                indicate integer variables

 VarWeight   Priorities for each variable in the variable selection phase
             A lower value gives higher priority. 

 fIP         An upper bound on the IP value wanted. Makes it possible to
             cut branches and avoid node computations.
 xIP         The x-values giving the fIP value.

Linear Constraints

 A           mA x n matrix A, linear constraints b_L<=A*x<=b_U. Dense or sparse
 b_L         Lower bound vector in linear constraints b_L <= A*x <= b_U. 
 b_U         Upper bound vector in linear constraints b_L <= A*x <= b_U. 

Nonlinear Constraints

 c           Name of function that computes the mN nonlinear constraints 
 dc          Name of function that computes the constraint Jacobian mN x n 
 c_L         Lower bound vector in nonlinear constraints b_L <= c(x) <= b_U. 
 c_U         Upper bound vector in nonlinear constraints b_L <= c(x) <= b_U. 
 ConsPattern mN x n zero-one sparse or dense matrix, where 0 values indicate 
             zeros in the constraint Jacobian and ones indicate values that 
             might be non-zero. Used when estimating the Jacobian numerically.

Additional Parameters

 x_min   Lower bounds on each x-variable, used for plotting
 x_max   Upper bounds on each x-variable, used for plotting
 f_opt   Optimal function value(s), if known (Stationary points)
 x_opt   The x-values corresponding to the given f_opt, if known.
         If only one f_opt, give x_opt as a 1 by n vector
         If several f_opt values, give x_opt as a length(f_opt) by n matrix
         If adding one extra column n+1 in x_opt, 0 is min, 1 saddle, 2 is max.
         x_opt and f_opt is used in printouts and plots.

Set the variable as empty if this variable is not needed for the particular kind of problem you are solving.

mcpAssign

mipAssign